Difference between revisions of "Contextual Literature-Based Discovery (C-LBD)"
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[https://www.bing.com/news/search?q=C-LBD+~Contextual+Literature-Based+Literature-Based+LBD&qft=interval%3d%228%22 ...Bing News] | [https://www.bing.com/news/search?q=C-LBD+~Contextual+Literature-Based+Literature-Based+LBD&qft=interval%3d%228%22 ...Bing News] | ||
| − | * [[ | + | * [[Perspective]] ... [[Context]] ... [[In-Context Learning (ICL)]] ... [[Transfer Learning]] ... [[Out-of-Distribution (OOD) Generalization]] |
| + | * [[Causation vs. Correlation]] ... [[Autocorrelation]] ...[[Convolution vs. Cross-Correlation (Autocorrelation)]] | ||
* [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]] | * [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]] | ||
* [https://arxiv.org/abs/2305.14259 Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery | Q. Wang, D. Downey, H. Ji, & T. Hope - arXiv - Cornell University] | * [https://arxiv.org/abs/2305.14259 Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery | Q. Wang, D. Downey, H. Ji, & T. Hope - arXiv - Cornell University] | ||
Latest revision as of 15:43, 28 April 2024
YouTube ... Quora ...Google search ...Google News ...Bing News
- Perspective ... Context ... In-Context Learning (ICL) ... Transfer Learning ... Out-of-Distribution (OOD) Generalization
- Causation vs. Correlation ... Autocorrelation ...Convolution vs. Cross-Correlation (Autocorrelation)
- Agents ... Robotic Process Automation ... Assistants ... Personal Companions ... Productivity ... Email ... Negotiation ... LangChain
- Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery | Q. Wang, D. Downey, H. Ji, & T. Hope - arXiv - Cornell University
- C-LBD | GitHub
Inspired from the idea that an AI assistant that can provide suggestions in plain English, including unique thoughts and connections.
Contextual Literature-Based Discovery (C-LBD) developed by researchers at the University of Illinois at Urbana-Champaign, the Hebrew University of Jerusalem, and the Allen Institute for Artificial Intelligence (AI2). C-LBD aims to address the limitations of traditional literature-based discovery (LBD) by using a natural language setting to constrain the generation space for LBD and generate sentences. The researchers introduce a novel modeling framework for C-LBD that can gather inspiration from disparate sources and use them to form novel hypotheses. They also introduce an in-context contrastive model to promote creative thinking. The team believes that expanding C-LBD to include a multimodal analysis of formulas, tables, and figures to provide a more comprehensive and enriched background context is an intriguing direction to investigate in the future. The use of advanced LLMs like GPT-4, which is currently in development, is another avenue to investigate. Can Language Models Generate New Scientific Ideas? Meet Contextualized Literature-Based Discovery (C-LBD) | Tanushree Shenwai - MarkTechPost
The assistant accepts as input (1) relevant information, such as present challenges, motives, and constraints, and (2) a seed phrase that should be the primary focus of the developed scientific concept. Given this information, C-LBD generates scientific hypotheses in natural language while grounding them in context that controls the hypothesis search space. C-LBD also takes into account critical context like experimental settings and background knowledge and motivations that human scientists. Two forms of C-LBD were investigated:
- one that generates a full phrase explaining an idea
- and another that generates only a salient component of the idea.
Literature-Based Discovery (LBD) in Healthcare